RAxML-NG v. 1.0.2 released on 22.02.2021 by The Exelixis Lab.
Developed by: Alexey M. Kozlov and Alexandros Stamatakis.
Contributors: Diego Darriba, Tomas Flouri, Benoit Morel, Sarah Lutteropp, Ben Bettisworth.
Latest version: https://github.com/amkozlov/raxml-ng
Questions/problems/suggestions? Please visit: https://groups.google.com/forum/#!forum/raxml

System: Intel(R) Xeon(R) Gold 6258R CPU @ 2.70GHz, 56 cores, 187 GB RAM

RAxML-NG was called at 26-Jul-2021 00:10:20 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/2_msa/O15263_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/3_mltree/O15263 --seed 2 --threads 1 --tree rand{20} pars{20}

Analysis options:
  run mode: ML tree search
  start tree(s): random (20)
  random seed: 2
  tip-inner: OFF
  pattern compression: ON
  per-rate scalers: OFF
  site repeats: ON
  fast spr radius: AUTO
  spr subtree cutoff: 1.000000
  branch lengths: proportional (ML estimate, algorithm: NR-FAST)
  SIMD kernels: AVX2
  parallelization: coarse-grained (auto), NONE/sequential

[00:00:00] Reading alignment from file: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/2_msa/O15263_trimmed_msa.fasta
[00:00:00] Loaded alignment with 72 taxa and 63 sites

WARNING: Sequences tr_A0A226MFK1_A0A226MFK1_CALSU_9009 and tr_A0A226PG14_A0A226PG14_COLVI_9014 are exactly identical!
WARNING: Duplicate sequences found: 1

NOTE: Reduced alignment (with duplicates and gap-only sites/taxa removed) 
NOTE: was saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/3_mltree/O15263.raxml.reduced.phy

Alignment comprises 1 partitions and 60 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 63 / 60
Gaps: 4.06 %
Invariant sites: 6.35 %


NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/3_mltree/O15263.raxml.rba

Parallelization scheme autoconfig: 1 worker(s) x 1 thread(s)

Parallel reduction/worker buffer size: 1 KB  / 0 KB

[00:00:00] Generating 20 random starting tree(s) with 72 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 60 / 4800
[00:00:00] Data distribution: max. searches per worker: 20

Starting ML tree search with 20 distinct starting trees

[00:00:00 -8930.578051] Initial branch length optimization
[00:00:00 -7376.680992] Model parameter optimization (eps = 10.000000)
[00:00:04 -7343.137320] AUTODETECT spr round 1 (radius: 5)
[00:00:05 -4617.654119] AUTODETECT spr round 2 (radius: 10)
[00:00:07 -4219.996226] AUTODETECT spr round 3 (radius: 15)
[00:00:09 -3957.975082] AUTODETECT spr round 4 (radius: 20)
[00:00:11 -3957.966128] SPR radius for FAST iterations: 15 (autodetect)
[00:00:11 -3957.966128] Model parameter optimization (eps = 3.000000)
[00:00:13 -3947.950656] FAST spr round 1 (radius: 15)
[00:00:16 -3676.080003] FAST spr round 2 (radius: 15)
[00:00:17 -3670.558684] FAST spr round 3 (radius: 15)
[00:00:19 -3669.201776] FAST spr round 4 (radius: 15)
[00:00:20 -3668.996410] FAST spr round 5 (radius: 15)
[00:00:21 -3668.996355] Model parameter optimization (eps = 1.000000)
[00:00:23 -3667.662812] SLOW spr round 1 (radius: 5)
[00:00:27 -3667.662700] SLOW spr round 2 (radius: 10)
[00:00:30 -3665.791866] SLOW spr round 3 (radius: 5)
[00:00:34 -3665.509905] SLOW spr round 4 (radius: 5)
[00:00:38 -3664.819041] SLOW spr round 5 (radius: 5)
[00:00:42 -3664.817427] SLOW spr round 6 (radius: 10)
[00:00:45 -3664.817352] SLOW spr round 7 (radius: 15)
[00:00:49 -3664.817348] SLOW spr round 8 (radius: 20)
[00:00:51 -3664.817348] SLOW spr round 9 (radius: 25)
[00:00:53 -3664.817348] Model parameter optimization (eps = 0.100000)

[00:00:55] ML tree search #1, logLikelihood: -3664.263093

[00:00:55 -8623.028331] Initial branch length optimization
[00:00:55 -7044.100963] Model parameter optimization (eps = 10.000000)
[00:00:58 -7021.527032] AUTODETECT spr round 1 (radius: 5)
[00:01:00 -4905.756200] AUTODETECT spr round 2 (radius: 10)
[00:01:02 -3980.867728] AUTODETECT spr round 3 (radius: 15)
[00:01:04 -3844.857241] AUTODETECT spr round 4 (radius: 20)
[00:01:05 -3844.854758] SPR radius for FAST iterations: 15 (autodetect)
[00:01:05 -3844.854758] Model parameter optimization (eps = 3.000000)
[00:01:07 -3834.872359] FAST spr round 1 (radius: 15)
[00:01:10 -3677.037764] FAST spr round 2 (radius: 15)
[00:01:12 -3665.831954] FAST spr round 3 (radius: 15)
[00:01:13 -3665.831761] Model parameter optimization (eps = 1.000000)
[00:01:14 -3665.438722] SLOW spr round 1 (radius: 5)
[00:01:17 -3665.437641] SLOW spr round 2 (radius: 10)
[00:01:21 -3665.437557] SLOW spr round 3 (radius: 15)
[00:01:25 -3665.437540] SLOW spr round 4 (radius: 20)
[00:01:27 -3665.437536] SLOW spr round 5 (radius: 25)
[00:01:29 -3665.437535] Model parameter optimization (eps = 0.100000)

[00:01:30] ML tree search #2, logLikelihood: -3665.436852

[00:01:30 -8588.713527] Initial branch length optimization
[00:01:30 -7073.494074] Model parameter optimization (eps = 10.000000)
[00:01:33 -7041.433245] AUTODETECT spr round 1 (radius: 5)
[00:01:34 -5098.398632] AUTODETECT spr round 2 (radius: 10)
[00:01:36 -4020.786907] AUTODETECT spr round 3 (radius: 15)
[00:01:38 -4006.627230] AUTODETECT spr round 4 (radius: 20)
[00:01:40 -4000.656640] AUTODETECT spr round 5 (radius: 25)
[00:01:41 -4000.641519] SPR radius for FAST iterations: 20 (autodetect)
[00:01:41 -4000.641519] Model parameter optimization (eps = 3.000000)
[00:01:43 -3986.520118] FAST spr round 1 (radius: 20)
[00:01:46 -3707.437094] FAST spr round 2 (radius: 20)
[00:01:48 -3667.438742] FAST spr round 3 (radius: 20)
[00:01:49 -3665.239234] FAST spr round 4 (radius: 20)
[00:01:51 -3665.239119] Model parameter optimization (eps = 1.000000)
[00:01:52 -3664.264342] SLOW spr round 1 (radius: 5)
[00:01:55 -3664.262395] SLOW spr round 2 (radius: 10)
[00:01:58 -3664.262370] SLOW spr round 3 (radius: 15)
[00:02:02 -3664.262369] SLOW spr round 4 (radius: 20)
[00:02:05 -3664.262368] SLOW spr round 5 (radius: 25)
[00:02:07 -3664.262368] Model parameter optimization (eps = 0.100000)

[00:02:07] ML tree search #3, logLikelihood: -3664.261619

[00:02:07 -8743.418954] Initial branch length optimization
[00:02:07 -7155.852575] Model parameter optimization (eps = 10.000000)
[00:02:09 -7147.016429] AUTODETECT spr round 1 (radius: 5)
[00:02:10 -5150.061316] AUTODETECT spr round 2 (radius: 10)
[00:02:13 -3992.116984] AUTODETECT spr round 3 (radius: 15)
[00:02:15 -3906.515344] AUTODETECT spr round 4 (radius: 20)
[00:02:16 -3906.514588] SPR radius for FAST iterations: 15 (autodetect)
[00:02:16 -3906.514588] Model parameter optimization (eps = 3.000000)
[00:02:19 -3897.024636] FAST spr round 1 (radius: 15)
[00:02:22 -3673.170439] FAST spr round 2 (radius: 15)
[00:02:23 -3670.060467] FAST spr round 3 (radius: 15)
[00:02:24 -3670.059663] Model parameter optimization (eps = 1.000000)
[00:02:25 -3669.575908] SLOW spr round 1 (radius: 5)
[00:02:29 -3668.272173] SLOW spr round 2 (radius: 5)
[00:02:32 -3668.119764] SLOW spr round 3 (radius: 5)
[00:02:35 -3668.119569] SLOW spr round 4 (radius: 10)
[00:02:38 -3665.626471] SLOW spr round 5 (radius: 5)
[00:02:43 -3665.623797] SLOW spr round 6 (radius: 10)
[00:02:46 -3665.623521] SLOW spr round 7 (radius: 15)
[00:02:50 -3665.623485] SLOW spr round 8 (radius: 20)
[00:02:53 -3665.623480] SLOW spr round 9 (radius: 25)
[00:02:55 -3665.623479] Model parameter optimization (eps = 0.100000)

[00:02:57] ML tree search #4, logLikelihood: -3664.202834

[00:02:57 -8655.181817] Initial branch length optimization
[00:02:57 -7125.504343] Model parameter optimization (eps = 10.000000)
[00:02:59 -7102.513853] AUTODETECT spr round 1 (radius: 5)
[00:03:01 -5005.292837] AUTODETECT spr round 2 (radius: 10)
[00:03:03 -3987.463323] AUTODETECT spr round 3 (radius: 15)
[00:03:05 -3987.317758] AUTODETECT spr round 4 (radius: 20)
[00:03:06 -3987.317049] SPR radius for FAST iterations: 15 (autodetect)
[00:03:06 -3987.317049] Model parameter optimization (eps = 3.000000)
[00:03:08 -3983.135184] FAST spr round 1 (radius: 15)
[00:03:10 -3679.879535] FAST spr round 2 (radius: 15)
[00:03:12 -3666.873054] FAST spr round 3 (radius: 15)
[00:03:14 -3666.791605] Model parameter optimization (eps = 1.000000)
[00:03:17 -3664.983849] SLOW spr round 1 (radius: 5)
[00:03:20 -3664.979918] SLOW spr round 2 (radius: 10)
[00:03:23 -3664.979658] SLOW spr round 3 (radius: 15)
[00:03:27 -3664.979626] SLOW spr round 4 (radius: 20)
[00:03:30 -3664.979621] SLOW spr round 5 (radius: 25)
[00:03:31 -3664.979620] Model parameter optimization (eps = 0.100000)

[00:03:32] ML tree search #5, logLikelihood: -3664.971553

[00:03:32 -8781.683735] Initial branch length optimization
[00:03:32 -7252.435287] Model parameter optimization (eps = 10.000000)
[00:03:36 -7175.816782] AUTODETECT spr round 1 (radius: 5)
[00:03:38 -4665.076858] AUTODETECT spr round 2 (radius: 10)
[00:03:40 -3986.593830] AUTODETECT spr round 3 (radius: 15)
[00:03:42 -3979.865098] AUTODETECT spr round 4 (radius: 20)
[00:03:43 -3979.847564] SPR radius for FAST iterations: 15 (autodetect)
[00:03:43 -3979.847564] Model parameter optimization (eps = 3.000000)
[00:03:47 -3964.780882] FAST spr round 1 (radius: 15)
[00:03:50 -3699.315319] FAST spr round 2 (radius: 15)
[00:03:51 -3688.036597] FAST spr round 3 (radius: 15)
[00:03:53 -3686.706845] FAST spr round 4 (radius: 15)
[00:03:54 -3686.381350] FAST spr round 5 (radius: 15)
[00:03:55 -3686.381278] Model parameter optimization (eps = 1.000000)
[00:03:59 -3680.843468] SLOW spr round 1 (radius: 5)
[00:04:02 -3680.472947] SLOW spr round 2 (radius: 5)
[00:04:05 -3680.472622] SLOW spr round 3 (radius: 10)
[00:04:08 -3680.472566] SLOW spr round 4 (radius: 15)
[00:04:12 -3680.472558] SLOW spr round 5 (radius: 20)
[00:04:15 -3680.472557] SLOW spr round 6 (radius: 25)
[00:04:17 -3680.472556] Model parameter optimization (eps = 0.100000)

[00:04:18] ML tree search #6, logLikelihood: -3680.459861

[00:04:18 -9108.715084] Initial branch length optimization
[00:04:18 -7480.833236] Model parameter optimization (eps = 10.000000)
[00:04:21 -7461.058022] AUTODETECT spr round 1 (radius: 5)
[00:04:22 -4428.215228] AUTODETECT spr round 2 (radius: 10)
[00:04:24 -3860.532669] AUTODETECT spr round 3 (radius: 15)
[00:04:26 -3860.506508] SPR radius for FAST iterations: 10 (autodetect)
[00:04:26 -3860.506508] Model parameter optimization (eps = 3.000000)
[00:04:29 -3850.410890] FAST spr round 1 (radius: 10)
[00:04:31 -3684.981058] FAST spr round 2 (radius: 10)
[00:04:33 -3669.242712] FAST spr round 3 (radius: 10)
[00:04:34 -3666.924999] FAST spr round 4 (radius: 10)
[00:04:36 -3666.923232] Model parameter optimization (eps = 1.000000)
[00:04:38 -3665.478090] SLOW spr round 1 (radius: 5)
[00:04:41 -3664.295137] SLOW spr round 2 (radius: 5)
[00:04:44 -3664.294687] SLOW spr round 3 (radius: 10)
[00:04:47 -3664.294638] SLOW spr round 4 (radius: 15)
[00:04:51 -3664.294628] SLOW spr round 5 (radius: 20)
[00:04:54 -3664.294626] SLOW spr round 6 (radius: 25)
[00:04:55 -3664.294625] Model parameter optimization (eps = 0.100000)

[00:04:56] ML tree search #7, logLikelihood: -3664.286935

[00:04:56 -8770.153640] Initial branch length optimization
[00:04:56 -7172.242974] Model parameter optimization (eps = 10.000000)
[00:05:02 -7139.639374] AUTODETECT spr round 1 (radius: 5)
[00:05:03 -4893.178729] AUTODETECT spr round 2 (radius: 10)
[00:05:05 -3836.379240] AUTODETECT spr round 3 (radius: 15)
[00:05:07 -3833.281355] AUTODETECT spr round 4 (radius: 20)
[00:05:09 -3833.239351] SPR radius for FAST iterations: 15 (autodetect)
[00:05:09 -3833.239351] Model parameter optimization (eps = 3.000000)
[00:05:12 -3824.032678] FAST spr round 1 (radius: 15)
[00:05:14 -3676.410280] FAST spr round 2 (radius: 15)
[00:05:16 -3673.922259] FAST spr round 3 (radius: 15)
[00:05:17 -3671.350345] FAST spr round 4 (radius: 15)
[00:05:19 -3667.422612] FAST spr round 5 (radius: 15)
[00:05:20 -3664.926478] FAST spr round 6 (radius: 15)
[00:05:22 -3664.926286] Model parameter optimization (eps = 1.000000)
[00:05:23 -3664.268937] SLOW spr round 1 (radius: 5)
[00:05:26 -3664.194617] SLOW spr round 2 (radius: 10)
[00:05:29 -3664.194531] SLOW spr round 3 (radius: 15)
[00:05:33 -3664.194518] SLOW spr round 4 (radius: 20)
[00:05:36 -3664.194515] SLOW spr round 5 (radius: 25)
[00:05:38 -3664.194515] Model parameter optimization (eps = 0.100000)

[00:05:38] ML tree search #8, logLikelihood: -3664.186253

[00:05:38 -9031.083334] Initial branch length optimization
[00:05:39 -7431.338444] Model parameter optimization (eps = 10.000000)
[00:05:42 -7415.954981] AUTODETECT spr round 1 (radius: 5)
[00:05:43 -5000.784560] AUTODETECT spr round 2 (radius: 10)
[00:05:45 -3952.782439] AUTODETECT spr round 3 (radius: 15)
[00:05:47 -3952.753349] SPR radius for FAST iterations: 10 (autodetect)
[00:05:47 -3952.753349] Model parameter optimization (eps = 3.000000)
[00:05:50 -3943.255936] FAST spr round 1 (radius: 10)
[00:05:53 -3687.951448] FAST spr round 2 (radius: 10)
[00:05:55 -3665.761570] FAST spr round 3 (radius: 10)
[00:05:56 -3665.761475] Model parameter optimization (eps = 1.000000)
[00:05:58 -3665.487808] SLOW spr round 1 (radius: 5)
[00:06:01 -3665.481216] SLOW spr round 2 (radius: 10)
[00:06:04 -3665.480872] SLOW spr round 3 (radius: 15)
[00:06:08 -3665.480831] SLOW spr round 4 (radius: 20)
[00:06:11 -3665.480824] SLOW spr round 5 (radius: 25)
[00:06:12 -3665.480823] Model parameter optimization (eps = 0.100000)

[00:06:14] ML tree search #9, logLikelihood: -3665.457664

[00:06:14 -8830.311367] Initial branch length optimization
[00:06:14 -7253.909664] Model parameter optimization (eps = 10.000000)
[00:06:17 -7187.464232] AUTODETECT spr round 1 (radius: 5)
[00:06:18 -4816.737799] AUTODETECT spr round 2 (radius: 10)
[00:06:20 -4159.491084] AUTODETECT spr round 3 (radius: 15)
[00:06:22 -3958.192068] AUTODETECT spr round 4 (radius: 20)
[00:06:23 -3958.188144] SPR radius for FAST iterations: 15 (autodetect)
[00:06:23 -3958.188144] Model parameter optimization (eps = 3.000000)
[00:06:29 -3934.511256] FAST spr round 1 (radius: 15)
[00:06:31 -3695.518491] FAST spr round 2 (radius: 15)
[00:06:33 -3683.353670] FAST spr round 3 (radius: 15)
[00:06:34 -3682.124631] FAST spr round 4 (radius: 15)
[00:06:35 -3682.124605] Model parameter optimization (eps = 1.000000)
[00:06:37 -3680.783986] SLOW spr round 1 (radius: 5)
[00:06:40 -3679.980356] SLOW spr round 2 (radius: 5)
[00:06:43 -3679.979151] SLOW spr round 3 (radius: 10)
[00:06:47 -3679.978943] SLOW spr round 4 (radius: 15)
[00:06:50 -3679.978908] SLOW spr round 5 (radius: 20)
[00:06:53 -3679.978902] SLOW spr round 6 (radius: 25)
[00:06:55 -3679.978901] Model parameter optimization (eps = 0.100000)

[00:06:55] ML tree search #10, logLikelihood: -3679.968527

[00:06:56 -9001.970337] Initial branch length optimization
[00:06:56 -7427.844434] Model parameter optimization (eps = 10.000000)
[00:06:59 -7410.997058] AUTODETECT spr round 1 (radius: 5)
[00:07:00 -4949.933580] AUTODETECT spr round 2 (radius: 10)
[00:07:02 -3917.424568] AUTODETECT spr round 3 (radius: 15)
[00:07:04 -3906.769912] AUTODETECT spr round 4 (radius: 20)
[00:07:05 -3906.764517] SPR radius for FAST iterations: 15 (autodetect)
[00:07:05 -3906.764517] Model parameter optimization (eps = 3.000000)
[00:07:08 -3893.527614] FAST spr round 1 (radius: 15)
[00:07:10 -3675.116510] FAST spr round 2 (radius: 15)
[00:07:12 -3670.491494] FAST spr round 3 (radius: 15)
[00:07:13 -3666.395611] FAST spr round 4 (radius: 15)
[00:07:15 -3666.392766] Model parameter optimization (eps = 1.000000)
[00:07:16 -3665.440782] SLOW spr round 1 (radius: 5)
[00:07:19 -3664.648729] SLOW spr round 2 (radius: 5)
[00:07:23 -3664.647838] SLOW spr round 3 (radius: 10)
[00:07:25 -3664.647786] SLOW spr round 4 (radius: 15)
[00:07:29 -3664.647780] SLOW spr round 5 (radius: 20)
[00:07:32 -3664.647780] SLOW spr round 6 (radius: 25)
[00:07:34 -3664.647779] Model parameter optimization (eps = 0.100000)

[00:07:35] ML tree search #11, logLikelihood: -3664.619539

[00:07:35 -8730.664813] Initial branch length optimization
[00:07:35 -6969.355952] Model parameter optimization (eps = 10.000000)
[00:07:36 -6962.621367] AUTODETECT spr round 1 (radius: 5)
[00:07:38 -4769.663367] AUTODETECT spr round 2 (radius: 10)
[00:07:40 -3838.210989] AUTODETECT spr round 3 (radius: 15)
[00:07:42 -3838.196289] SPR radius for FAST iterations: 10 (autodetect)
[00:07:42 -3838.196289] Model parameter optimization (eps = 3.000000)
[00:07:44 -3828.183426] FAST spr round 1 (radius: 10)
[00:07:47 -3678.170650] FAST spr round 2 (radius: 10)
[00:07:48 -3668.679637] FAST spr round 3 (radius: 10)
[00:07:50 -3666.929257] FAST spr round 4 (radius: 10)
[00:07:51 -3666.929042] Model parameter optimization (eps = 1.000000)
[00:07:54 -3665.753087] SLOW spr round 1 (radius: 5)
[00:07:58 -3664.232515] SLOW spr round 2 (radius: 5)
[00:08:01 -3664.231789] SLOW spr round 3 (radius: 10)
[00:08:04 -3664.231704] SLOW spr round 4 (radius: 15)
[00:08:07 -3664.231692] SLOW spr round 5 (radius: 20)
[00:08:10 -3664.231691] SLOW spr round 6 (radius: 25)
[00:08:12 -3664.231690] Model parameter optimization (eps = 0.100000)

[00:08:13] ML tree search #12, logLikelihood: -3664.186345

[00:08:13 -8899.871974] Initial branch length optimization
[00:08:13 -7332.561348] Model parameter optimization (eps = 10.000000)
[00:08:15 -7310.379459] AUTODETECT spr round 1 (radius: 5)
[00:08:17 -4244.943526] AUTODETECT spr round 2 (radius: 10)
[00:08:19 -3871.458434] AUTODETECT spr round 3 (radius: 15)
[00:08:21 -3871.418358] SPR radius for FAST iterations: 10 (autodetect)
[00:08:21 -3871.418358] Model parameter optimization (eps = 3.000000)
[00:08:23 -3861.189493] FAST spr round 1 (radius: 10)
[00:08:25 -3672.863933] FAST spr round 2 (radius: 10)
[00:08:27 -3668.298838] FAST spr round 3 (radius: 10)
[00:08:28 -3665.718782] FAST spr round 4 (radius: 10)
[00:08:30 -3665.717516] Model parameter optimization (eps = 1.000000)
[00:08:32 -3664.620096] SLOW spr round 1 (radius: 5)
[00:08:35 -3664.619485] SLOW spr round 2 (radius: 10)
[00:08:38 -3664.619436] SLOW spr round 3 (radius: 15)
[00:08:42 -3664.619428] SLOW spr round 4 (radius: 20)
[00:08:45 -3664.619426] SLOW spr round 5 (radius: 25)
[00:08:46 -3664.619426] Model parameter optimization (eps = 0.100000)

[00:08:47] ML tree search #13, logLikelihood: -3664.619357

[00:08:47 -8596.780471] Initial branch length optimization
[00:08:47 -7083.050661] Model parameter optimization (eps = 10.000000)
[00:08:50 -6981.306525] AUTODETECT spr round 1 (radius: 5)
[00:08:51 -4995.899989] AUTODETECT spr round 2 (radius: 10)
[00:08:53 -4014.901152] AUTODETECT spr round 3 (radius: 15)
[00:08:56 -3976.752560] AUTODETECT spr round 4 (radius: 20)
[00:08:57 -3976.744999] SPR radius for FAST iterations: 15 (autodetect)
[00:08:57 -3976.744999] Model parameter optimization (eps = 3.000000)
[00:09:01 -3959.684614] FAST spr round 1 (radius: 15)
[00:09:03 -3749.325966] FAST spr round 2 (radius: 15)
[00:09:05 -3685.805141] FAST spr round 3 (radius: 15)
[00:09:07 -3684.626194] FAST spr round 4 (radius: 15)
[00:09:08 -3684.626177] Model parameter optimization (eps = 1.000000)
[00:09:12 -3682.999074] SLOW spr round 1 (radius: 5)
[00:09:15 -3682.103993] SLOW spr round 2 (radius: 5)
[00:09:18 -3682.103369] SLOW spr round 3 (radius: 10)
[00:09:21 -3682.103311] SLOW spr round 4 (radius: 15)
[00:09:25 -3682.103301] SLOW spr round 5 (radius: 20)
[00:09:28 -3682.103300] SLOW spr round 6 (radius: 25)
[00:09:30 -3682.103299] Model parameter optimization (eps = 0.100000)

[00:09:30] ML tree search #14, logLikelihood: -3682.084313

[00:09:30 -8840.818023] Initial branch length optimization
[00:09:31 -7114.669009] Model parameter optimization (eps = 10.000000)
[00:09:34 -7076.293850] AUTODETECT spr round 1 (radius: 5)
[00:09:35 -4687.853082] AUTODETECT spr round 2 (radius: 10)
[00:09:37 -3993.702428] AUTODETECT spr round 3 (radius: 15)
[00:09:40 -3968.882319] AUTODETECT spr round 4 (radius: 20)
[00:09:41 -3968.881403] SPR radius for FAST iterations: 15 (autodetect)
[00:09:41 -3968.881403] Model parameter optimization (eps = 3.000000)
[00:09:44 -3957.752912] FAST spr round 1 (radius: 15)
[00:09:47 -3698.978681] FAST spr round 2 (radius: 15)
[00:09:49 -3691.326147] FAST spr round 3 (radius: 15)
[00:09:50 -3691.072447] FAST spr round 4 (radius: 15)
[00:09:51 -3690.623341] FAST spr round 5 (radius: 15)
[00:09:53 -3689.593499] FAST spr round 6 (radius: 15)
[00:09:54 -3689.593150] Model parameter optimization (eps = 1.000000)
[00:09:57 -3681.233509] SLOW spr round 1 (radius: 5)
[00:10:00 -3680.682892] SLOW spr round 2 (radius: 5)
[00:10:03 -3680.682706] SLOW spr round 3 (radius: 10)
[00:10:06 -3680.682702] SLOW spr round 4 (radius: 15)
[00:10:10 -3680.682702] SLOW spr round 5 (radius: 20)
[00:10:13 -3680.682702] SLOW spr round 6 (radius: 25)
[00:10:15 -3680.682702] Model parameter optimization (eps = 0.100000)

[00:10:16] ML tree search #15, logLikelihood: -3680.675230

[00:10:16 -8811.184103] Initial branch length optimization
[00:10:16 -7157.520124] Model parameter optimization (eps = 10.000000)
[00:10:19 -7103.796750] AUTODETECT spr round 1 (radius: 5)
[00:10:20 -4883.291167] AUTODETECT spr round 2 (radius: 10)
[00:10:22 -4215.430911] AUTODETECT spr round 3 (radius: 15)
[00:10:24 -4149.336582] AUTODETECT spr round 4 (radius: 20)
[00:10:26 -4149.330701] SPR radius for FAST iterations: 15 (autodetect)
[00:10:26 -4149.330701] Model parameter optimization (eps = 3.000000)
[00:10:31 -4122.177134] FAST spr round 1 (radius: 15)
[00:10:34 -3691.100482] FAST spr round 2 (radius: 15)
[00:10:35 -3684.349403] FAST spr round 3 (radius: 15)
[00:10:37 -3682.517392] FAST spr round 4 (radius: 15)
[00:10:38 -3682.165293] FAST spr round 5 (radius: 15)
[00:10:40 -3682.165170] Model parameter optimization (eps = 1.000000)
[00:10:43 -3680.462251] SLOW spr round 1 (radius: 5)
[00:10:46 -3680.459937] SLOW spr round 2 (radius: 10)
[00:10:49 -3680.459901] SLOW spr round 3 (radius: 15)
[00:10:53 -3680.459897] SLOW spr round 4 (radius: 20)
[00:10:55 -3680.459896] SLOW spr round 5 (radius: 25)
[00:10:57 -3680.459896] Model parameter optimization (eps = 0.100000)

[00:10:57] ML tree search #16, logLikelihood: -3680.459878

[00:10:57 -8925.580940] Initial branch length optimization
[00:10:58 -7426.405865] Model parameter optimization (eps = 10.000000)
[00:11:02 -7398.879650] AUTODETECT spr round 1 (radius: 5)
[00:11:04 -4623.961925] AUTODETECT spr round 2 (radius: 10)
[00:11:06 -3802.573525] AUTODETECT spr round 3 (radius: 15)
[00:11:08 -3795.143033] AUTODETECT spr round 4 (radius: 20)
[00:11:09 -3795.140641] SPR radius for FAST iterations: 15 (autodetect)
[00:11:09 -3795.140641] Model parameter optimization (eps = 3.000000)
[00:11:12 -3789.636812] FAST spr round 1 (radius: 15)
[00:11:14 -3690.711125] FAST spr round 2 (radius: 15)
[00:11:16 -3670.332091] FAST spr round 3 (radius: 15)
[00:11:18 -3667.484900] FAST spr round 4 (radius: 15)
[00:11:19 -3667.484388] Model parameter optimization (eps = 1.000000)
[00:11:23 -3666.181537] SLOW spr round 1 (radius: 5)
[00:11:26 -3665.164766] SLOW spr round 2 (radius: 5)
[00:11:29 -3665.164579] SLOW spr round 3 (radius: 10)
[00:11:33 -3665.164576] SLOW spr round 4 (radius: 15)
[00:11:36 -3665.164576] SLOW spr round 5 (radius: 20)
[00:11:39 -3665.164576] SLOW spr round 6 (radius: 25)
[00:11:41 -3665.164576] Model parameter optimization (eps = 0.100000)

[00:11:42] ML tree search #17, logLikelihood: -3665.143590

[00:11:42 -8778.827248] Initial branch length optimization
[00:11:42 -7235.116705] Model parameter optimization (eps = 10.000000)
[00:11:45 -7219.637445] AUTODETECT spr round 1 (radius: 5)
[00:11:46 -4591.613748] AUTODETECT spr round 2 (radius: 10)
[00:11:48 -3902.721546] AUTODETECT spr round 3 (radius: 15)
[00:11:50 -3902.714888] SPR radius for FAST iterations: 10 (autodetect)
[00:11:50 -3902.714888] Model parameter optimization (eps = 3.000000)
[00:11:52 -3900.060529] FAST spr round 1 (radius: 10)
[00:11:54 -3674.484080] FAST spr round 2 (radius: 10)
[00:11:56 -3670.188830] FAST spr round 3 (radius: 10)
[00:11:57 -3670.187749] Model parameter optimization (eps = 1.000000)
[00:12:00 -3666.565638] SLOW spr round 1 (radius: 5)
[00:12:03 -3664.647666] SLOW spr round 2 (radius: 5)
[00:12:06 -3664.647594] SLOW spr round 3 (radius: 10)
[00:12:09 -3664.647584] SLOW spr round 4 (radius: 15)
[00:12:13 -3664.647582] SLOW spr round 5 (radius: 20)
[00:12:16 -3664.647582] SLOW spr round 6 (radius: 25)
[00:12:18 -3664.647582] Model parameter optimization (eps = 0.100000)

[00:12:18] ML tree search #18, logLikelihood: -3664.619466

[00:12:18 -8543.465533] Initial branch length optimization
[00:12:18 -6867.249026] Model parameter optimization (eps = 10.000000)
[00:12:20 -6860.481148] AUTODETECT spr round 1 (radius: 5)
[00:12:22 -4822.408634] AUTODETECT spr round 2 (radius: 10)
[00:12:24 -3882.789997] AUTODETECT spr round 3 (radius: 15)
[00:12:25 -3873.882888] AUTODETECT spr round 4 (radius: 20)
[00:12:27 -3873.879508] SPR radius for FAST iterations: 15 (autodetect)
[00:12:27 -3873.879508] Model parameter optimization (eps = 3.000000)
[00:12:30 -3863.890598] FAST spr round 1 (radius: 15)
[00:12:32 -3692.026713] FAST spr round 2 (radius: 15)
[00:12:34 -3673.428874] FAST spr round 3 (radius: 15)
[00:12:36 -3667.635003] FAST spr round 4 (radius: 15)
[00:12:38 -3666.881485] FAST spr round 5 (radius: 15)
[00:12:39 -3666.881364] Model parameter optimization (eps = 1.000000)
[00:12:41 -3664.336165] SLOW spr round 1 (radius: 5)
[00:12:44 -3664.335824] SLOW spr round 2 (radius: 10)
[00:12:47 -3664.335798] SLOW spr round 3 (radius: 15)
[00:12:51 -3664.335794] SLOW spr round 4 (radius: 20)
[00:12:54 -3664.335793] SLOW spr round 5 (radius: 25)
[00:12:55 -3664.335792] Model parameter optimization (eps = 0.100000)

[00:12:55] ML tree search #19, logLikelihood: -3664.335787

[00:12:56 -8786.203569] Initial branch length optimization
[00:12:56 -7226.816860] Model parameter optimization (eps = 10.000000)
[00:12:58 -7199.532319] AUTODETECT spr round 1 (radius: 5)
[00:13:00 -4580.276630] AUTODETECT spr round 2 (radius: 10)
[00:13:02 -3806.237803] AUTODETECT spr round 3 (radius: 15)
[00:13:04 -3804.027016] AUTODETECT spr round 4 (radius: 20)
[00:13:05 -3803.987007] SPR radius for FAST iterations: 15 (autodetect)
[00:13:05 -3803.987007] Model parameter optimization (eps = 3.000000)
[00:13:08 -3795.433967] FAST spr round 1 (radius: 15)
[00:13:10 -3688.519387] FAST spr round 2 (radius: 15)
[00:13:12 -3676.615044] FAST spr round 3 (radius: 15)
[00:13:14 -3666.702055] FAST spr round 4 (radius: 15)
[00:13:15 -3666.701926] Model parameter optimization (eps = 1.000000)
[00:13:17 -3665.381043] SLOW spr round 1 (radius: 5)
[00:13:21 -3665.302481] SLOW spr round 2 (radius: 10)
[00:13:24 -3665.302354] SLOW spr round 3 (radius: 15)
[00:13:28 -3665.302328] SLOW spr round 4 (radius: 20)
[00:13:30 -3665.302323] SLOW spr round 5 (radius: 25)
[00:13:32 -3665.302322] Model parameter optimization (eps = 0.100000)

[00:13:32] ML tree search #20, logLikelihood: -3665.293910


Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.392971,0.395826) (0.205562,0.702619) (0.314407,1.533670) (0.087059,2.502017) 
   Base frequencies (model): 
M0: 0.147383 0.017579 0.058208 0.017707 0.026331 0.041582 0.017494 0.027859 0.011849 0.076971 0.147823 0.019535 0.037132 0.029940 0.008059 0.088179 0.089653 0.006477 0.032308 0.097931 
M1: 0.063139 0.066357 0.011586 0.066571 0.010800 0.009276 0.053984 0.146986 0.034214 0.088822 0.098196 0.032390 0.021263 0.072697 0.016761 0.020711 0.020797 0.025463 0.045615 0.094372 
M2: 0.062457 0.066826 0.049332 0.065270 0.006513 0.041231 0.058965 0.080852 0.028024 0.037024 0.075925 0.064131 0.019620 0.028710 0.104579 0.056388 0.062027 0.008241 0.033124 0.050760 
M3: 0.106471 0.074171 0.044513 0.096390 0.002148 0.066733 0.158908 0.037625 0.020691 0.014608 0.028797 0.105352 0.007864 0.007477 0.083595 0.055726 0.047711 0.003975 0.010088 0.027159 
   Substitution rates (model): 
M 0: 0.295719 0.067388 0.253712 1.029289 0.107964 0.514644 10.868848 0.380498 0.084223 0.086976 0.188789 0.286389 0.155567 1.671061 2.132922 0.529591 0.115551 0.102453 0.916683 0.448317 0.457483 0.576016 1.741924 0.736017 0.704334 5.658311 0.123387 0.221777 93.433377 0.382175 0.235965 6.535048 0.525521 0.303537 0.641259 0.289466 0.102065 2.358429 0.251987 0.216561 0.503084 0.435271 4.873453 0.090748 0.033310 0.746537 0.128905 0.127321 0.904011 0.939733 0.435450 0.046646 0.262076 0.043986 0.189008 0.599450 109.901504 1.070052 5.229858 0.052764 0.021407 0.621146 0.081091 0.205164 5.164456 0.747330 0.308078 0.260889 0.185083 0.080708 0.029955 0.084794 1.862626 0.553477 0.151733 0.230320 0.096955 0.352526 0.590018 0.386853 1.559564 0.606648 0.587531 0.592318 0.885230 4.117654 0.246260 6.508329 0.054187 0.195703 1.669092 0.810168 0.066081 2.437439 0.165666 0.106333 0.093417 0.035149 0.072549 1.202023 1.634845 0.060194 0.069359 2.448827 0.232297 0.064822 3.537387 0.435384 0.290413 0.280695 0.105999 0.206603 0.404968 0.048984 0.069963 0.256662 0.228519 0.241077 4.320442 3.656545 0.290216 0.307466 0.096556 0.306067 0.204296 0.504221 1.991533 0.655465 6.799829 11.291065 0.961142 0.448965 6.227274 20.304886 0.205944 1.495537 0.091940 1.994320 0.754940 0.170343 0.050315 0.372166 0.206332 0.097050 5.381403 0.122332 3.256485 2.261319 0.848067 0.064441 0.102493 0.459041 0.133091 0.561215 0.457430 0.163849 5.260446 0.360946 0.389413 0.033291 0.115301 0.112593 1.559944 0.426508 0.132547 0.498634 0.559069 0.264728 0.693307 0.438856 0.306683 0.109129 18.392863 66.647302 0.400021 4.586081 2.099355 0.411347 0.476350 0.584622 3.634276 0.101797 0.148995 0.089177 0.034710 0.063603 0.755865 20.561407 0.133790 0.154902 
M 1: 0.066142 0.590377 0.069930 9.850951 1.101363 0.150375 0.568586 0.051668 0.127170 0.292429 0.071458 1.218562 0.075144 7.169085 30.139501 13.461692 0.021372 0.045779 4.270235 0.468325 0.013688 0.302287 1.353957 0.028386 0.037750 0.262130 0.016923 0.064289 0.855973 0.079621 0.011169 0.161937 0.276530 0.161053 0.081472 0.036742 0.030342 2.851667 3.932151 8.159169 0.219934 0.421974 2.468752 0.344765 0.210724 1.172204 0.763553 0.082464 0.726566 11.149790 4.782635 0.058046 0.498072 0.258487 0.146882 0.249672 0.560142 0.046719 0.106259 0.003656 0.004200 0.014189 0.009876 0.002656 0.040244 0.267322 0.053740 0.006597 0.027639 0.012745 0.582670 0.005035 0.275844 0.098208 0.445038 1.217010 0.033969 1.988516 0.681161 0.825960 18.762977 11.949233 0.286794 0.534219 4.336817 3.054085 0.129551 4.210126 0.165753 1.088704 1.889645 3.344809 0.111063 2.067758 3.547017 2.466507 0.188236 0.203493 0.281953 0.037250 0.029788 0.008541 0.014768 0.125869 0.056702 0.004186 0.110993 0.201148 0.139705 0.009201 0.012095 0.043812 0.013513 0.002533 0.005848 0.031390 0.021612 0.004854 0.129497 0.976631 0.053397 0.019475 0.004964 0.015539 0.031779 0.064558 0.065585 0.079927 0.095591 0.196886 0.408834 0.126088 0.037226 0.452302 0.016212 7.278994 0.029917 7.918203 0.450964 0.169797 0.104288 1.578530 0.015909 0.094365 16.179952 0.042762 14.799537 1.506485 0.637893 0.123793 0.641351 0.154810 0.140750 3.416059 0.259400 0.009457 0.090576 0.292108 0.297913 0.017172 0.021976 0.032578 1.375871 0.457399 0.598048 4.418398 0.239749 0.168432 2.950318 0.143327 0.328689 0.125011 0.562720 1.414883 0.227807 3.478333 2.984862 0.061299 0.077470 1.050562 13.974326 0.154326 0.224675 0.112000 0.060703 0.123480 5.294490 0.447011 0.033381 0.045528 
M 2: 0.733336 0.558955 0.503360 4.149599 1.415369 1.367574 1.263002 0.994098 0.517204 0.775054 0.763094 1.890137 0.540460 0.200122 4.972745 1.825593 0.450842 0.526135 3.839269 0.597671 0.058964 2.863355 2.872594 0.258365 0.366868 2.578946 0.358350 0.672023 5.349861 0.691594 0.063347 0.032875 0.821562 0.580847 0.661866 0.265730 0.395134 5.581680 1.279881 1.335650 0.397108 1.840061 5.739035 0.284730 0.109781 1.612642 0.466979 0.141582 0.019509 4.670980 1.967383 0.088064 0.581928 0.145401 0.225860 0.434096 2.292917 1.024707 0.821921 0.027824 0.021443 0.088850 0.060820 0.018288 0.042687 1.199607 0.420710 0.037642 0.141233 0.090101 1.043232 0.209978 0.823594 3.039380 1.463390 1.983693 0.397640 2.831098 4.102068 0.059723 5.901348 2.034980 2.600668 5.413080 4.193725 4.534772 0.377181 4.877840 0.370939 1.298542 3.509873 2.646440 0.087872 0.072299 1.139018 0.864479 0.390688 0.322761 0.625409 0.496780 0.532488 0.232460 0.169219 0.755219 0.379926 0.020447 0.023282 0.503875 0.577513 0.109318 0.153776 0.696533 0.398817 0.008940 0.043707 0.436013 0.087640 0.064863 0.036426 1.673207 0.124068 0.218118 0.039217 0.104335 0.349195 0.838324 0.888693 0.488389 1.385133 0.050226 0.962470 0.502294 1.065585 8.351808 0.377304 5.102837 0.561690 7.010411 3.054968 0.039318 0.204155 2.653232 0.564368 0.854294 15.559906 0.401070 8.929538 5.525874 0.067505 0.273372 0.437116 1.927515 0.940458 2.508169 1.357738 0.043394 0.023126 0.567639 1.048288 0.120994 0.180650 0.449074 3.135353 0.012695 0.570771 2.319555 1.856122 0.975427 3.404087 0.015631 0.458799 0.151684 4.154750 11.429924 1.457957 0.233109 0.077004 0.011074 0.026268 0.052132 8.113282 0.377578 0.429221 0.260296 0.222293 0.273138 2.903836 4.731579 0.564762 0.681215 
M 3: 0.658412 0.566269 0.854111 0.884454 1.309554 1.272639 1.874713 0.552007 0.227683 0.581512 0.695190 0.967985 0.344015 0.978992 3.427163 2.333253 0.154701 0.221089 2.088785 0.540749 0.058015 5.851132 2.294145 0.182966 0.684164 3.192521 0.528161 1.128882 3.010922 1.012866 0.227296 0.156635 0.878405 0.802754 0.830884 0.431617 0.456530 3.060574 1.279257 1.438430 0.431464 2.075952 4.840271 0.644656 0.266076 2.084975 0.720060 0.291854 0.028961 4.071574 2.258357 0.073037 1.238426 0.199728 0.160296 0.482619 2.992763 1.296206 0.841829 0.031467 0.048542 0.132774 0.133055 0.056045 0.209188 0.925172 0.360522 0.094591 0.313945 0.118104 0.992259 0.086318 2.149634 5.103188 3.775817 3.954021 0.190734 1.776095 4.495841 0.264277 7.063879 2.221150 3.017954 8.558815 4.310199 2.130054 0.571406 4.137385 0.437589 2.071689 2.498630 1.763546 0.116381 0.296578 1.033710 1.283423 0.312579 0.305772 0.681277 0.507160 0.351381 0.189152 0.217780 0.767361 0.278392 0.092075 0.177263 0.451893 0.653836 0.074620 0.181992 0.752277 0.679853 0.025780 0.082005 0.326441 0.343977 0.195877 0.217424 3.057583 0.377558 0.401252 0.072258 0.241015 0.665865 1.266791 0.680174 0.717301 4.001286 0.362942 1.189259 0.964545 1.350568 12.869737 0.531100 8.904999 0.652629 10.091413 2.671718 0.086367 0.359932 4.797423 0.336801 1.021885 23.029406 0.440178 14.013035 5.069337 0.539010 0.742569 0.780580 1.331875 1.531589 4.414850 1.082703 0.091278 0.172734 0.693405 1.422571 0.068958 0.163829 0.481711 4.643214 0.121821 0.584083 4.216178 1.677263 1.575754 5.046403 0.161015 1.531223 0.599244 5.832025 33.873091 1.914768 1.287474 0.444362 0.076328 0.079916 0.466823 5.231362 0.548763 0.831890 0.382271 0.208791 0.307846 3.717971 5.910440 0.282540 0.964421 


Final LogLikelihood: -3664.186253

AIC score: 7622.372506 / AICc score: 51134.372506 / BIC score: 7937.413310
Free parameters (model + branch lengths): 147

WARNING: Number of free parameters (K=147) is larger than alignment size (n=63).
         This might lead to overfitting and compromise tree inference results!


WARNING: Best ML tree contains 8 near-zero branches!

Best ML tree with collapsed near-zero branches saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/3_mltree/O15263.raxml.bestTreeCollapsed
Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/3_mltree/O15263.raxml.bestTree
All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/3_mltree/O15263.raxml.mlTrees
Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/3_mltree/O15263.raxml.bestModel

Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/O15263/3_mltree/O15263.raxml.log

Analysis started: 26-Jul-2021 00:10:20 / finished: 26-Jul-2021 00:23:52

Elapsed time: 812.763 seconds

Consumed energy: 40.344 Wh